Effectiveness of “brain—computer” interfaces with biofeedback in the rehabilitation of cognitive impairment after a stroke
Bibliographic record
Abstract
Objective. Comparison of the effectiveness of two “brain—computer” interface (BCI) software complexes using biofeedback (BF) and standard therapy in restoring cognitive functions after a stroke. Material and methods. Eighty-nine stroke patients were examined. Neuropsychological testing was carried out using the Montreal Cognitive Assessment Scale (MoCA), the Tracking test, the Wechsler subtest 9 Kohs block design test, the Schulte tables, the Memorization of 10 Words test (according to A.R. Luria). Using the simple randomization method, three groups were formed: the main group (n=37), the comparison group (n=33) and the control group (n=19). In Group 1, sessions were conducted with BCI+BF based on the rhythm P300; in Group 2, with BCI+BF based on the mu-rhythm of electroencephalography (EEG), Group 3 received standard therapy. Results. An increase in the total MoCA score was reported in all three groups. The results in Groups 1 and 2 were comparable, exceeding those in Group 3 (p1—2=0.199, p1—3<0.001, p2—3=0.037). The effectiveness in Group 1 did not depend on the baseline MoCA score, exceeding the indicators in Group 3; in Group 2, the advantage over Group 3 was with a baseline MoCA of at least 22. According to the Schulte tables and the Tracking test, comparable statistically significant changes were obtained in Groups 1 and 2; no statistically significant change was reported in the control group. The Kohs block design test showed a more statistically significant change in the main group. The Memorization of 10 Words test by A.R. Luria also showed a more consistent improvement in mnestic disorders in the main group. Conclusion. The effectiveness of BCI+BF exceeded standard therapy for post-stroke cognitive impairment. The advantage of IMC+BFB used in the main group over IMC+BFB in the comparison group was noted, which was due to a decrease in the effectiveness of the latter with a baseline MoCA score of less than 22 points, lower performance in the Memorizing 10 Words test and the Kohs block design test.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".